Functional vegetation metrics are more reliable for remotely sensed resilience

Resilience is a key property reflecting how ecosystems cope with stress, disturbances, and environmental change. Current estimates of remotely sensed resilience based on critical slowing down are subject to inconsistency, potentially leading to false warnings of ecosystem collapse. When it comes to remote sensing-based resilience indicators, the choice of vegetation indices remains a fundamental but unresolved issue. Here, we used time series data of remotely sensed vegetation structure (characterized by leaf area index, LAI) and function (characterized by solar-induced chlorophyll fluorescence, SIF) to compare the critical-slowing-down based resilience indicators with the indicators representing recovery rate upon disturbance events. We aim to quantify which vegetation metric (structural versus functional) provides more reliable resilience estimates, as reflected by higher consistency between these two classes of resilience indicators. Our results show that the reliability of SIF is, on average, more than twice that of LAI, and the area suitable for estimating resilience with SIF is 4.83 times higher. SIF-based estimates indicated that the number of pixels with declining resilience was 1.07 times higher than those with increasing resilience. In contrast, LAI-based estimates overestimate the proportion of pixels with declining resilience by 35.5 %. Our study reveals a mismatch between the recovery rates of vegetation structure and function, and supports the use of function-based metrics for resilience estimation. The proposed reliability assessment framework addresses inconsistencies in resilience estimates from the perspective of data source selection. We recommend prioritizing vegetation functional indicators in most scenarios. Employing appropriate vegetation indicators for resilience estimation will benefit global and regional sustainable ecosystem management and early warning systems.